Overview

Dataset statistics

Number of variables13
Number of observations7415
Missing cells57
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory753.2 KiB
Average record size in memory104.0 B

Variable types

Numeric11
Text2

Alerts

Proteins_Carbs_Ratio has 432 (5.8%) infinite valuesInfinite
Grams is highly skewed (γ1 = 21.86649389)Skewed
Fiber is highly skewed (γ1 = 29.65209972)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
Protein has 265 (3.6%) zerosZeros
Fat has 373 (5.0%) zerosZeros
Sat.Fat has 508 (6.9%) zerosZeros
Fiber has 1784 (24.1%) zerosZeros
Carbs has 487 (6.6%) zerosZeros
Proteins_per_gram has 265 (3.6%) zerosZeros
Proteins_Carbs_Ratio has 210 (2.8%) zerosZeros

Reproduction

Analysis started2023-12-11 14:55:59.547318
Analysis finished2023-12-11 14:56:24.242502
Duration24.7 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct7415
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3709.9496
Minimum0
Maximum7417
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-12-11T20:26:24.435982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile373.7
Q11856.5
median3710
Q35563.5
95-th percentile7046.3
Maximum7417
Range7417
Interquartile range (IQR)3707

Descriptive statistics

Standard deviation2140.7561
Coefficient of variation (CV)0.57703105
Kurtosis-1.1998264
Mean3709.9496
Median Absolute Deviation (MAD)1854
Skewness-0.00013310607
Sum27509276
Variance4582836.6
MonotonicityStrictly increasing
2023-12-11T20:26:24.570916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
4956 1
 
< 0.1%
4954 1
 
< 0.1%
4953 1
 
< 0.1%
4952 1
 
< 0.1%
4951 1
 
< 0.1%
4950 1
 
< 0.1%
4949 1
 
< 0.1%
4948 1
 
< 0.1%
4947 1
 
< 0.1%
Other values (7405) 7405
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
7417 1
< 0.1%
7416 1
< 0.1%
7415 1
< 0.1%
7414 1
< 0.1%
7413 1
< 0.1%
7412 1
< 0.1%
7411 1
< 0.1%
7410 1
< 0.1%
7409 1
< 0.1%
7408 1
< 0.1%

Food
Text

Distinct7393
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size58.1 KiB
2023-12-11T20:26:24.839782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length184
Median length110
Mean length38.651382
Min length3

Characters and Unicode

Total characters286600
Distinct characters73
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7373 ?
Unique (%)99.4%

Sample

1st rowCows' milk
2nd rowMilk skim
3rd rowButtermilk
4th rowEvaporated, undiluted
5th rowFortified milk
ValueCountFrequency (%)
with 2245
 
5.0%
or 1543
 
3.4%
and 1221
 
2.7%
fat 1055
 
2.4%
added 631
 
1.4%
sauce 621
 
1.4%
cooked 596
 
1.3%
as 588
 
1.3%
to 577
 
1.3%
ns 561
 
1.3%
Other values (1890) 35140
78.5%
2023-12-11T20:26:25.565213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37381
 
13.0%
e 28894
 
10.1%
a 22437
 
7.8%
o 17602
 
6.1%
t 17099
 
6.0%
r 16261
 
5.7%
d 13257
 
4.6%
i 13218
 
4.6%
n 12516
 
4.4%
, 12432
 
4.3%
Other values (63) 95503
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 222234
77.5%
Space Separator 37381
 
13.0%
Other Punctuation 13145
 
4.6%
Uppercase Letter 11556
 
4.0%
Dash Punctuation 1137
 
0.4%
Open Punctuation 447
 
0.2%
Close Punctuation 447
 
0.2%
Decimal Number 253
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28894
13.0%
a 22437
 
10.1%
o 17602
 
7.9%
t 17099
 
7.7%
r 16261
 
7.3%
d 13257
 
6.0%
i 13218
 
5.9%
n 12516
 
5.6%
s 10980
 
4.9%
c 9494
 
4.3%
Other values (16) 60476
27.2%
Uppercase Letter
ValueCountFrequency (%)
C 1933
16.7%
S 1740
15.1%
P 1271
11.0%
N 970
8.4%
B 777
 
6.7%
F 749
 
6.5%
M 504
 
4.4%
R 495
 
4.3%
T 458
 
4.0%
G 394
 
3.4%
Other values (16) 2265
19.6%
Other Punctuation
ValueCountFrequency (%)
, 12432
94.6%
/ 331
 
2.5%
; 186
 
1.4%
' 74
 
0.6%
% 68
 
0.5%
" 29
 
0.2%
. 18
 
0.1%
& 6
 
< 0.1%
: 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 109
43.1%
1 78
30.8%
2 41
 
16.2%
5 10
 
4.0%
4 7
 
2.8%
3 5
 
2.0%
9 2
 
0.8%
7 1
 
0.4%
Space Separator
ValueCountFrequency (%)
37381
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1137
100.0%
Open Punctuation
ValueCountFrequency (%)
( 447
100.0%
Close Punctuation
ValueCountFrequency (%)
) 447
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 233790
81.6%
Common 52810
 
18.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28894
12.4%
a 22437
 
9.6%
o 17602
 
7.5%
t 17099
 
7.3%
r 16261
 
7.0%
d 13257
 
5.7%
i 13218
 
5.7%
n 12516
 
5.4%
s 10980
 
4.7%
c 9494
 
4.1%
Other values (42) 72032
30.8%
Common
ValueCountFrequency (%)
37381
70.8%
, 12432
 
23.5%
- 1137
 
2.2%
( 447
 
0.8%
) 447
 
0.8%
/ 331
 
0.6%
; 186
 
0.4%
0 109
 
0.2%
1 78
 
0.1%
' 74
 
0.1%
Other values (11) 188
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 286600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37381
 
13.0%
e 28894
 
10.1%
a 22437
 
7.8%
o 17602
 
6.1%
t 17099
 
6.0%
r 16261
 
5.7%
d 13257
 
4.6%
i 13218
 
4.6%
n 12516
 
4.4%
, 12432
 
4.3%
Other values (63) 95503
33.3%

Grams
Real number (ℝ)

SKEWED 

Distinct103
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.94889
Minimum11
Maximum1419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-12-11T20:26:25.944861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile100
Q1100
median100
Q3100
95-th percentile100
Maximum1419
Range1408
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.766728
Coefficient of variation (CV)0.30178581
Kurtosis720.95593
Mean101.94889
Median Absolute Deviation (MAD)0
Skewness21.866494
Sum755951
Variance946.59153
MonotonicityNot monotonic
2023-12-11T20:26:26.347592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7120
96.0%
85 26
 
0.4%
250 20
 
0.3%
50 18
 
0.2%
150 10
 
0.1%
200 10
 
0.1%
28 9
 
0.1%
120 8
 
0.1%
40 7
 
0.1%
14 7
 
0.1%
Other values (93) 180
 
2.4%
ValueCountFrequency (%)
11 1
 
< 0.1%
12 1
 
< 0.1%
14 7
0.1%
15 3
 
< 0.1%
16 1
 
< 0.1%
17 2
 
< 0.1%
20 5
0.1%
23 3
 
< 0.1%
25 3
 
< 0.1%
28 9
0.1%
ValueCountFrequency (%)
1419 1
 
< 0.1%
984 1
 
< 0.1%
976 1
 
< 0.1%
925 1
 
< 0.1%
540 1
 
< 0.1%
480 1
 
< 0.1%
454 2
 
< 0.1%
380 1
 
< 0.1%
346 6
0.1%
300 1
 
< 0.1%

Calories
Real number (ℝ)

Distinct5527
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.95067
Minimum0
Maximum3969
Zeros30
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-12-11T20:26:26.781809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.808
Q181.94
median163.95
Q3274.75
95-th percentile476.2
Maximum3969
Range3969
Interquartile range (IQR)192.81

Descriptive statistics

Standard deviation155.6334
Coefficient of variation (CV)0.78227129
Kurtosis52.249395
Mean198.95067
Median Absolute Deviation (MAD)93.51
Skewness3.3699542
Sum1475219.2
Variance24221.755
MonotonicityNot monotonic
2023-12-11T20:26:27.434145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
0.4%
65.33 20
 
0.3%
900 16
 
0.2%
66.53 16
 
0.2%
65.54 16
 
0.2%
66.41 15
 
0.2%
205.26 13
 
0.2%
177.67 11
 
0.1%
66.04 11
 
0.1%
327.3 10
 
0.1%
Other values (5517) 7257
97.9%
ValueCountFrequency (%)
0 30
0.4%
0.32 1
 
< 0.1%
0.4 9
 
0.1%
0.48 1
 
< 0.1%
0.52 1
 
< 0.1%
0.53 1
 
< 0.1%
0.54 1
 
< 0.1%
0.56 1
 
< 0.1%
0.6 1
 
< 0.1%
0.66 5
 
0.1%
ValueCountFrequency (%)
3969 1
 
< 0.1%
1963 2
 
< 0.1%
1210 1
 
< 0.1%
1207 1
 
< 0.1%
1182 1
 
< 0.1%
990 1
 
< 0.1%
900.26 1
 
< 0.1%
900 16
0.2%
899.73 1
 
< 0.1%
896.44 1
 
< 0.1%

Protein
Real number (ℝ)

ZEROS 

Distinct2118
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5985597
Minimum-1
Maximum232
Zeros265
Zeros (%)3.6%
Negative1
Negative (%)< 0.1%
Memory size58.1 KiB
2023-12-11T20:26:27.810872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.12
Q12.12
median6.04
Q312.1
95-th percentile25.129
Maximum232
Range233
Interquartile range (IQR)9.98

Descriptive statistics

Standard deviation8.9385533
Coefficient of variation (CV)1.0395408
Kurtosis61.515451
Mean8.5985597
Median Absolute Deviation (MAD)4.45
Skewness3.9636904
Sum63758.32
Variance79.897735
MonotonicityNot monotonic
2023-12-11T20:26:28.181266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 265
 
3.6%
1 65
 
0.9%
2 56
 
0.8%
3 42
 
0.6%
0.1 35
 
0.5%
4 30
 
0.4%
0.2 29
 
0.4%
1.4 29
 
0.4%
0.3 29
 
0.4%
5 26
 
0.4%
Other values (2108) 6809
91.8%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0 265
3.6%
0.01 3
 
< 0.1%
0.02 5
 
0.1%
0.03 5
 
0.1%
0.04 8
 
0.1%
0.05 4
 
0.1%
0.06 4
 
0.1%
0.07 5
 
0.1%
0.08 14
 
0.2%
ValueCountFrequency (%)
232 1
< 0.1%
114 2
< 0.1%
89 1
< 0.1%
78.13 2
< 0.1%
76.25 1
< 0.1%
66.67 1
< 0.1%
64.06 1
< 0.1%
62.82 2
< 0.1%
61.3 1
< 0.1%
58.94 1
< 0.1%

Fat
Real number (ℝ)

ZEROS 

Distinct2113
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9417181
Minimum0
Maximum233
Zeros373
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-12-11T20:26:28.522597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.91
median5.4
Q312.54
95-th percentile27.775
Maximum233
Range233
Interquartile range (IQR)10.63

Descriptive statistics

Standard deviation11.587472
Coefficient of variation (CV)1.2958888
Kurtosis36.492601
Mean8.9417181
Median Absolute Deviation (MAD)4.62
Skewness4.2298029
Sum66302.84
Variance134.26952
MonotonicityNot monotonic
2023-12-11T20:26:28.911443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 373
 
5.0%
0.1 77
 
1.0%
0.2 51
 
0.7%
1 33
 
0.4%
0.3 32
 
0.4%
3.49 31
 
0.4%
0.08 31
 
0.4%
0.02 29
 
0.4%
0.07 26
 
0.4%
3.41 25
 
0.3%
Other values (2103) 6707
90.5%
ValueCountFrequency (%)
0 373
5.0%
0.01 18
 
0.2%
0.02 29
 
0.4%
0.03 13
 
0.2%
0.04 14
 
0.2%
0.05 11
 
0.1%
0.06 11
 
0.1%
0.07 26
 
0.4%
0.08 31
 
0.4%
0.09 15
 
0.2%
ValueCountFrequency (%)
233 1
 
< 0.1%
115 2
 
< 0.1%
110 1
 
< 0.1%
100 16
0.2%
99.98 1
 
< 0.1%
99.97 1
 
< 0.1%
99.48 1
 
< 0.1%
99.1 1
 
< 0.1%
92.18 1
 
< 0.1%
91 1
 
< 0.1%

Sat.Fat
Real number (ℝ)

ZEROS 

Distinct3505
Distinct (%)47.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.9598288
Minimum0
Maximum234
Zeros508
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-12-11T20:26:29.295471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.452
median1.436
Q33.726
95-th percentile10.0776
Maximum234
Range234
Interquartile range (IQR)3.274

Descriptive statistics

Standard deviation5.6575493
Coefficient of variation (CV)1.9114448
Kurtosis449.90668
Mean2.9598288
Median Absolute Deviation (MAD)1.322
Skewness14.71713
Sum21941.211
Variance32.007864
MonotonicityNot monotonic
2023-12-11T20:26:29.682978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 508
 
6.9%
0.002 40
 
0.5%
0.4 19
 
0.3%
0.014 18
 
0.2%
0.009 18
 
0.2%
1 18
 
0.2%
0.02 17
 
0.2%
1.429 17
 
0.2%
0.018 17
 
0.2%
1.24 16
 
0.2%
Other values (3495) 6725
90.7%
ValueCountFrequency (%)
0 508
6.9%
0.001 9
 
0.1%
0.002 40
 
0.5%
0.003 10
 
0.1%
0.004 8
 
0.1%
0.005 9
 
0.1%
0.006 12
 
0.2%
0.007 5
 
0.1%
0.008 14
 
0.2%
0.009 18
 
0.2%
ValueCountFrequency (%)
234 1
 
< 0.1%
116 2
< 0.1%
92 1
 
< 0.1%
88 1
 
< 0.1%
82.5 1
 
< 0.1%
76 1
 
< 0.1%
61.924 1
 
< 0.1%
51.368 3
< 0.1%
46.24 1
 
< 0.1%
46.235 1
 
< 0.1%

Fiber
Real number (ℝ)

SKEWED  ZEROS 

Distinct164
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7342293
Minimum0
Maximum235
Zeros1784
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-12-11T20:26:30.061229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median1
Q32.1
95-th percentile6.2
Maximum235
Range235
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.2286466
Coefficient of variation (CV)2.4383435
Kurtosis1410.283
Mean1.7342293
Median Absolute Deviation (MAD)1
Skewness29.6521
Sum12859.31
Variance17.881452
MonotonicityNot monotonic
2023-12-11T20:26:30.437702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1784
24.1%
0.6 319
 
4.3%
1 226
 
3.0%
1.2 218
 
2.9%
0.2 209
 
2.8%
0.4 206
 
2.8%
0.9 205
 
2.8%
0.7 196
 
2.6%
1.1 196
 
2.6%
0.3 193
 
2.6%
Other values (154) 3663
49.4%
ValueCountFrequency (%)
0 1784
24.1%
0.1 180
 
2.4%
0.2 209
 
2.8%
0.3 193
 
2.6%
0.31 1
 
< 0.1%
0.4 206
 
2.8%
0.5 145
 
2.0%
0.6 319
 
4.3%
0.7 196
 
2.6%
0.8 188
 
2.5%
ValueCountFrequency (%)
235 1
< 0.1%
117 2
< 0.1%
67.5 1
< 0.1%
46.2 1
< 0.1%
42.8 1
< 0.1%
37.5 1
< 0.1%
37 1
< 0.1%
29.3 1
< 0.1%
27.3 2
< 0.1%
26.9 1
< 0.1%

Carbs
Real number (ℝ)

ZEROS 

Distinct3151
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.020243
Minimum0
Maximum236
Zeros487
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-12-11T20:26:30.704165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.6
median13.3
Q326.46
95-th percentile71.823
Maximum236
Range236
Interquartile range (IQR)20.86

Descriptive statistics

Standard deviation22.718211
Coefficient of variation (CV)1.0807778
Kurtosis5.3970119
Mean21.020243
Median Absolute Deviation (MAD)9.17
Skewness1.8709267
Sum155865.1
Variance516.11711
MonotonicityNot monotonic
2023-12-11T20:26:31.189384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 487
 
6.6%
0.1 88
 
1.2%
12.51 36
 
0.5%
9.85 30
 
0.4%
13.1 30
 
0.4%
6.87 23
 
0.3%
1 21
 
0.3%
9.86 16
 
0.2%
9 16
 
0.2%
7.45 15
 
0.2%
Other values (3141) 6653
89.7%
ValueCountFrequency (%)
0 487
6.6%
0.01 3
 
< 0.1%
0.03 2
 
< 0.1%
0.04 3
 
< 0.1%
0.05 2
 
< 0.1%
0.06 9
 
0.1%
0.07 5
 
0.1%
0.08 3
 
< 0.1%
0.09 12
 
0.2%
0.1 88
 
1.2%
ValueCountFrequency (%)
236 1
< 0.1%
229 1
< 0.1%
216 1
< 0.1%
210 1
< 0.1%
199 1
< 0.1%
154 1
< 0.1%
150 1
< 0.1%
142 2
< 0.1%
134 1
< 0.1%
119 1
< 0.1%
Distinct2445
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Memory size58.1 KiB
2023-12-11T20:26:31.798659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length79
Median length62
Mean length14.131895
Min length3

Characters and Unicode

Total characters104788
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1753 ?
Unique (%)23.6%

Sample

1st rowDairy products
2nd rowDairy products
3rd rowDairy products
4th rowDairy products
5th rowDairy products
ValueCountFrequency (%)
or 665
 
3.9%
with 664
 
3.9%
and 494
 
2.9%
chicken 475
 
2.8%
sandwich 297
 
1.8%
egg 285
 
1.7%
rice 271
 
1.6%
beef 265
 
1.6%
vegetables 235
 
1.4%
sauce 234
 
1.4%
Other values (1347) 13034
77.0%
2023-12-11T20:26:32.939212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 11121
 
10.6%
9507
 
9.1%
a 8756
 
8.4%
r 6564
 
6.3%
o 6181
 
5.9%
t 5957
 
5.7%
i 5508
 
5.3%
s 5415
 
5.2%
n 4805
 
4.6%
l 3792
 
3.6%
Other values (61) 37182
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 85476
81.6%
Space Separator 9507
 
9.1%
Uppercase Letter 8756
 
8.4%
Other Punctuation 403
 
0.4%
Dash Punctuation 266
 
0.3%
Open Punctuation 185
 
0.2%
Close Punctuation 181
 
0.2%
Decimal Number 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11121
13.0%
a 8756
 
10.2%
r 6564
 
7.7%
o 6181
 
7.2%
t 5957
 
7.0%
i 5508
 
6.4%
s 5415
 
6.3%
n 4805
 
5.6%
l 3792
 
4.4%
c 3789
 
4.4%
Other values (16) 23588
27.6%
Uppercase Letter
ValueCountFrequency (%)
C 1766
20.2%
P 1133
12.9%
B 775
 
8.9%
S 743
 
8.5%
F 574
 
6.6%
M 451
 
5.2%
T 411
 
4.7%
R 365
 
4.2%
G 300
 
3.4%
E 271
 
3.1%
Other values (16) 1967
22.5%
Other Punctuation
ValueCountFrequency (%)
, 272
67.5%
' 62
 
15.4%
/ 37
 
9.2%
" 22
 
5.5%
& 6
 
1.5%
% 2
 
0.5%
; 1
 
0.2%
: 1
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 4
28.6%
2 3
21.4%
3 2
14.3%
5 2
14.3%
1 1
 
7.1%
9 1
 
7.1%
7 1
 
7.1%
Space Separator
ValueCountFrequency (%)
9507
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 266
100.0%
Open Punctuation
ValueCountFrequency (%)
( 185
100.0%
Close Punctuation
ValueCountFrequency (%)
) 181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 94232
89.9%
Common 10556
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11121
 
11.8%
a 8756
 
9.3%
r 6564
 
7.0%
o 6181
 
6.6%
t 5957
 
6.3%
i 5508
 
5.8%
s 5415
 
5.7%
n 4805
 
5.1%
l 3792
 
4.0%
c 3789
 
4.0%
Other values (42) 32344
34.3%
Common
ValueCountFrequency (%)
9507
90.1%
, 272
 
2.6%
- 266
 
2.5%
( 185
 
1.8%
) 181
 
1.7%
' 62
 
0.6%
/ 37
 
0.4%
" 22
 
0.2%
& 6
 
0.1%
0 4
 
< 0.1%
Other values (9) 14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11121
 
10.6%
9507
 
9.1%
a 8756
 
8.4%
r 6564
 
6.3%
o 6181
 
5.9%
t 5957
 
5.7%
i 5508
 
5.3%
s 5415
 
5.2%
n 4805
 
4.6%
l 3792
 
3.6%
Other values (61) 37182
35.5%

Calorie_per_gram
Real number (ℝ)

Distinct5630
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9932234
Minimum0
Maximum17.526786
Zeros30
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-12-11T20:26:33.296767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3015
Q10.8074
median1.6434
Q32.77205
95-th percentile4.80455
Maximum17.526786
Range17.526786
Interquartile range (IQR)1.96465

Descriptive statistics

Standard deviation1.5030449
Coefficient of variation (CV)0.75407746
Kurtosis5.9019842
Mean1.9932234
Median Absolute Deviation (MAD)0.9499
Skewness1.5523425
Sum14779.752
Variance2.2591438
MonotonicityNot monotonic
2023-12-11T20:26:33.722531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
0.4%
0.6533 20
 
0.3%
9 20
 
0.3%
0.6653 16
 
0.2%
0.6554 16
 
0.2%
0.6641 15
 
0.2%
2.0526 13
 
0.2%
1.7767 11
 
0.1%
0.6604 11
 
0.1%
3.273 10
 
0.1%
Other values (5620) 7253
97.8%
ValueCountFrequency (%)
0 30
0.4%
0.0032 1
 
< 0.1%
0.004 9
 
0.1%
0.0048 1
 
< 0.1%
0.0052 1
 
< 0.1%
0.0053 1
 
< 0.1%
0.0054 1
 
< 0.1%
0.0056 1
 
< 0.1%
0.006 1
 
< 0.1%
0.0066 5
 
0.1%
ValueCountFrequency (%)
17.52678571 2
 
< 0.1%
17.25652174 1
 
< 0.1%
9.0026 1
 
< 0.1%
9 20
0.3%
8.9973 1
 
< 0.1%
8.9644 1
 
< 0.1%
8.9526 1
 
< 0.1%
8.45 1
 
< 0.1%
8.3854 1
 
< 0.1%
7.8794 1
 
< 0.1%

Proteins_per_gram
Real number (ℝ)

ZEROS 

Distinct2237
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.085683165
Minimum-0.016666667
Maximum1.0178571
Zeros265
Zeros (%)3.6%
Negative1
Negative (%)< 0.1%
Memory size58.1 KiB
2023-12-11T20:26:34.133455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.016666667
5-th percentile0.0012
Q10.0213
median0.0605
Q30.12065
95-th percentile0.25293
Maximum1.0178571
Range1.0345238
Interquartile range (IQR)0.09935

Descriptive statistics

Standard deviation0.085390602
Coefficient of variation (CV)0.99658552
Kurtosis9.5294088
Mean0.085683165
Median Absolute Deviation (MAD)0.0446
Skewness2.0334828
Sum635.34067
Variance0.0072915548
MonotonicityNot monotonic
2023-12-11T20:26:34.549346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 265
 
3.6%
0.001 35
 
0.5%
0.003 29
 
0.4%
0.014 29
 
0.4%
0.002 29
 
0.4%
0.02 28
 
0.4%
0.016 26
 
0.4%
0.03 24
 
0.3%
0.0138 23
 
0.3%
0.008 19
 
0.3%
Other values (2227) 6908
93.2%
ValueCountFrequency (%)
-0.01666666667 1
 
< 0.1%
0 265
3.6%
0.0001 3
 
< 0.1%
0.0002 5
 
0.1%
0.0003 5
 
0.1%
0.0004 8
 
0.1%
0.0005 4
 
0.1%
0.0006 4
 
0.1%
0.0007 5
 
0.1%
0.0008 14
 
0.2%
ValueCountFrequency (%)
1.017857143 2
< 0.1%
1.008695652 1
< 0.1%
0.7813 2
< 0.1%
0.7625 1
< 0.1%
0.6667 1
< 0.1%
0.6406 1
< 0.1%
0.6282 2
< 0.1%
0.613 1
< 0.1%
0.5894 1
< 0.1%
0.5814 1
< 0.1%

Proteins_Carbs_Ratio
Real number (ℝ)

INFINITE  ZEROS 

Distinct5377
Distinct (%)73.1%
Missing55
Missing (%)0.7%
Infinite432
Infinite (%)5.8%
Meaninf
Minimum-0.05
Maximuminf
Zeros210
Zeros (%)2.8%
Negative1
Negative (%)< 0.1%
Memory size58.1 KiB
2023-12-11T20:26:34.959241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.05
5-th percentile0.018061532
Q10.1198709
median0.3142055
Q31.0898038
95-th percentilenan
Maximuminf
Rangeinf
Interquartile range (IQR)0.96993293

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meaninf
Median Absolute Deviation (MAD)0.23689745
Skewnessnan
Suminf
Variancenan
MonotonicityNot monotonic
2023-12-11T20:26:35.343314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
inf 432
 
5.8%
0 210
 
2.8%
0.1994177584 16
 
0.2%
0.1986577181 15
 
0.2%
1 14
 
0.2%
0.3333333333 11
 
0.1%
0.1938202247 11
 
0.1%
0.189701897 10
 
0.1%
0.2242090784 10
 
0.1%
0.234769688 10
 
0.1%
Other values (5367) 6621
89.3%
(Missing) 55
 
0.7%
ValueCountFrequency (%)
-0.05 1
 
< 0.1%
0 210
2.8%
0.0003318584071 1
 
< 0.1%
0.0009094009321 1
 
< 0.1%
0.001009081736 1
 
< 0.1%
0.001117318436 1
 
< 0.1%
0.001154734411 1
 
< 0.1%
0.001178473222 1
 
< 0.1%
0.001217656012 1
 
< 0.1%
0.001221747098 1
 
< 0.1%
ValueCountFrequency (%)
inf 432
5.8%
1700 1
 
< 0.1%
1163 2
 
< 0.1%
942.6666667 2
 
< 0.1%
687 1
 
< 0.1%
529.2 1
 
< 0.1%
382.6666667 6
 
0.1%
374 2
 
< 0.1%
351.5714286 3
 
< 0.1%
323.6666667 6
 
0.1%

Interactions

2023-12-11T20:26:22.244762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:04.578479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:07.741765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:10.917605image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:13.509626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:14.710495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:16.149783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:17.261062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:18.357171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:19.382403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:20.922536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:22.397648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:04.868314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:08.019192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:11.199422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:13.727978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:14.807773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:16.269700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:17.371340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:18.445836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:19.486163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:21.054522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:22.534782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:05.143879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:08.288452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:11.466617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:13.823560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:14.906141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:16.376576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:17.510403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:18.542968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:19.592375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:21.183032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:22.668421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:05.426997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:08.629819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:11.742955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:13.911167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:15.217589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:16.477637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:17.648490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:18.645223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:19.706687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:21.314375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:22.783224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:05.674279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:09.046891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:11.970456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:14.008248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:15.315578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:16.560529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:17.745305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:18.744651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:19.804298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:21.420414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:22.893199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:05.917839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:09.488002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:12.175717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:14.095410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:15.432698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:16.651488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:17.830864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:18.827774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:19.930314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:21.532753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:23.005002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:06.155457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:09.730587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:12.380493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:14.181010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:15.546665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:16.758282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:17.908565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:18.903185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:20.050731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:21.643359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:23.118049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:06.395415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:09.952376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:12.580877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:14.274723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:15.650508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:16.853171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:18.001792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:19.007551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:20.170295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:21.749648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:23.238406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:06.643537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:10.179690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:12.768479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:14.370899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:15.737670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:16.959646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:18.078799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:19.079113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:20.290276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:21.857697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:23.369323image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:06.992002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:10.439280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:12.978526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:14.495852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:15.847294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:17.069369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:18.178317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:19.190311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:20.423078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:21.982556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:23.489409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:07.440900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:10.691236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:13.243447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:14.602350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:15.959267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:17.162435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:18.265810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:19.282717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:20.769018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-11T20:26:22.094638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2023-12-11T20:26:23.667753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T20:26:23.986568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0FoodGramsCaloriesProteinFatSat.FatFiberCarbsCategoryCalorie_per_gramProteins_per_gramProteins_Carbs_Ratio
00Cows' milk976.0680.032.040.036.00.048.0Dairy products0.6967210.0327870.666667
11Milk skim984.0352.036.00.00.00.052.0Dairy products0.3577240.0365850.692308
22Buttermilk246.0133.09.05.04.00.013.0Dairy products0.5406500.0365850.692308
33Evaporated, undiluted252.0340.016.020.018.00.024.0Dairy products1.3492060.0634920.666667
44Fortified milk1419.01210.089.042.023.01.4119.0Dairy products0.8527130.0627200.747899
55Powdered milk103.0516.027.028.024.00.039.0Dairy products5.0097090.2621360.692308
66skim, instant85.0288.030.00.00.00.042.0Dairy products3.3882350.3529410.714286
77skim, non-instant85.0288.030.00.00.01.042.0Dairy products3.3882350.3529410.714286
88Goats' milk244.0166.08.010.08.00.011.0Dairy products0.6803280.0327870.727273
99(1/2 cup ice cream)540.0592.024.024.022.00.070.0Dairy products1.0962960.0444440.342857
Unnamed: 0FoodGramsCaloriesProteinFatSat.FatFiberCarbsCategoryCalorie_per_gramProteins_per_gramProteins_Carbs_Ratio
74057408Cauliflower, cooked, as ingredient100.031.332.000.290.1352.15.18Cauliflower0.31330.02000.386100
74067409Eggplant, cooked, as ingredient100.031.191.050.190.0373.26.32Eggplant0.31190.01050.166139
74077410Green beans, cooked, as ingredient100.038.751.910.230.0522.87.26Green beans0.38750.01910.263085
74087411Summer squash, cooked, as ingredient100.024.831.310.350.1041.24.11Summer squash0.24830.01310.318735
74097412Dark green vegetables as ingredient in omelet100.037.002.970.400.0782.55.38Dark green vegetables as ingredient in omelet0.37000.02970.552045
74107413Tomatoes as ingredient in omelet100.028.431.110.230.0381.65.48Tomatoes as ingredient in omelet0.28430.01110.202555
74117414Other vegetables as ingredient in omelet100.036.503.460.380.0611.44.81Other vegetables as ingredient in omelet0.36500.03460.719335
74127415Vegetables as ingredient in curry100.055.351.810.190.0512.211.60Vegetables as ingredient in curry0.55350.01810.156034
74137416Sauce as ingredient in hamburgers100.0279.571.3422.853.5440.617.14Sauce as ingredient in hamburgers2.79570.01340.078180
74147417Industrial oil as ingredient in food100.0900.000.00100.0032.6720.00.00Industrial oil as ingredient in food9.00000.0000NaN